Taxiing Time Prediction Based on Graph Convolutional Network
To accurately predict taxiing time,this study proposes a graph convolutional neural network prediction method based on the evolution of airport surface operation situation.Firstly,based on the spati-otemporal distribution of aircraft on the airport surface,a traffic situation indicator system is constructed from multiple perspectives such as road flow,road density,and road speed.Secondly,the principal compo-nent analysis method is used to reduce the dimensionality of the indicators and the K-means algorithm is used to achieve the classification of the situation level of the airport surface road sections,and to draw a spatiotemporal distribution heatmap of the airport surface.Finally,using Graph Convolutional Neural Net-work(GCN)combined with Gated Recurrent Unit(GRU)to obtain the spatiotemporal features of the road segment feature data,the GRU is used as the decoder to predict the output sliding time.This study takes the simulation data of AirTOP at Shenzhen Bao'an International Airport as an example to analyze and verify the proposed method,and obtains expected prediction results.The experimental results indicate that this method is effective in predicting taxi time.
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